System and method for the dynamic analysis of event data

ABSTRACT

Disclosed is a system and method for the analysis of event data that enables analysts to create user specified datasets in a dynamic fashion. Performance, equipment and system safety, reliability, and significant event analysis utilizes failure or performance data that are composed in part of time-based records. These data identify the temporal occurrence of performance changes that may necessitate scheduled or unscheduled intervention like maintenance events, trades, purchases, or other actions to take advantage of mitigate or compensate for the observed changes. The criteria used to prompt a failure or performance record can range from complete loss of function to subtle changes in performance parameters that are known to be precursors of more severe events. These specific criteria applied to any explicit specific application and this invention is relevant to this type of data taxonomy and can be applied across all areas in which event data may be collected.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser.No. 61/794,403, filed Mar. 15, 2013, which is incorporated by referencein its entirety.

STATEMENTS REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

REFERENCE TO A MICROFICHE APPENDIX

Not applicable.

FIELD

The present disclosure relates to the analysis of event data, e.g.,safety, reliability, and performance data, which enables analysts tocreate user specified datasets in a dynamic fashion. Datasets may beanalyzed by statistical analysis tools to identify trends in event data,patterns in the time between events that can provide insights regardingevent causality, prediction estimates or to compute optimal inspectionintervals.

BACKGROUND AND BRIEF SUMMARY

Disclosed is a system and method for the analysis of event data thatenables analysts to create user specified datasets in a dynamic fashion.Performance, equipment and system safety, reliability, and significantevent analysis utilizes failure or performance data that are composed inpart of time-based records. These data identify the temporal occurrenceof performance changes that may necessitate unscheduled interventionlike maintenance events, or other actions to mitigate or compensate forthe observed changes. The criteria used to prompt a failure orperformance record can range from complete loss of function to subtlechanges in performance parameters that are known to be precursors ofmore severe events. These specific criteria applied to any explicitspecific application and this invention is relevant to this type of datataxonomy.

The concept of failure or performance is not universally defined. In thecase of machinery by way of example only, it depends on the equipment orsystem itself, its mission or role, the applied monitoring technology,and the risk appetite of the system owner. Subsequently, reliabilitydata or failure or performance data is more accurately labeled as“event” data that can relate to safety incidents, insurance, situationsrequiring maintenance or operational changes, the occurrence ofprecursor conditions from condition monitoring, external effects,security (stock bond, mutual fund, etc.) fluctuations and othersignificant events that influence operations or other decisions.Reliability data is defined as temporal based information collected at asub-component, component, sub-system, system and other possiblecategorical levels that documents performance levels achievingpre-defined values or ranges. The methods, systems and related dataanalyzed according to the present invention can relate to, e.g.,facilities such as processing or manufacturing plants, industries (e.g.,medical, airline, social media, telecommunications, oil and gas,chemicals, hydrocarbon processing, pharmaceutical, biotechnology)securities markets, weather, housing and commercial real estate markets,and any other application where data may be collected for analysis offailure or performance.

Systems and methods disclosed herein may also be applied to non-physicalor generalized systems where the concept of maintenance, intervention,control or analogous concepts may or may not apply. Examples of suchnon-physical systems are stock value fluctuations, economic indicators,and the occurrence of weather events. This type of data is calledsignificant event data and defined as temporal-based data recordsreflecting either changes in or the achievement of specific pre-setvalues.

In addition to event identification, another fundamental aspect ofreliability refers to the description detail used to record the eventcharacteristics. A classification structure or taxonomy is an extremelyvaluable aspect of reliability measurement in that it records the reasonor reasons why the failure or performance occurred. Standard failure orperformance classification systems exist but their use is dependent onregulatory and commercial management directions. Maintaining accuratefailure or performance data requires discipline that it can haveconsiderable benefits. The term failure or performance data as usedherein not only means data associated with the failure or performanceand/or reliability of equipment and machinery, but one skilled in theart will also understand that failure or performance data means dataassociated with any measure or classification that fails to meetperformance, reliability thresholds or other criteria of interest. Inother words, when data “fails to meet” it can be because it was lower orhigher than a threshold. Moreover, robust failure or performancedescriptions detailing the system, subsystems, equipment, financialinstrument, insurance product, purchasing criteria, safety criteria,internet search criteria, security criteria, component, and failure orperformance and/or reliability mode, for example, can help analystsidentify systemic failure or performances and create data-drivenprograms such as reliability improvement. However, failure orperformance and significant event taxonomies and their use vary bycompany and sometimes by location inside the same company.

In addition to data taxonomy considerations and the temporal recordingof reliability and significant events, another data attribute usingthese characteristics is the value of data elements at time of datarecordation. This type of data is referred to as condition monitoringthat, for example could be vibration or pressure readings of a pumprecorded at specified time, wheel brake pad thicknesses recorded duringinspections, reactor vessel thicknesses recorded during unit overhauls,daily stock values, and any other value of interest. The recording timesmay or may not be at fixed intervals.

The most accurate analysis of equipment and system reliability requiresdata and expert insights on how to identify systematic patterns infailure or performance data. It is the identification and subsequentanalysis of these relatively minor failure or performances that canprevent the large catastrophic events, e.g., resulting in loss, injury,and/or devaluation in equipment, money, value, personnel, systems orother interests. This statement is supported by the root cause analysisof large failure or performance events. The post accident analysis shoesthat many accidents are the end results of a sequence of less severe,often seemingly innocuous events that together in tandem enabled orallowed the large failure or performance to occur. This is also seen inthe technical analysis of stocks or other financial instruments when keysupport levels are violated or when companies announce hiring freezes orlayoffs causing a ripple effect. It is a common conclusion in thesereports that the major failure or performance would not have occurred ifany one of the precursor events had been prevented or otherwise had notoccurred or occurred at the levels that caused or otherwise resulted inthe effect It will be understood by one skilled in the art that theopposite is also true, e.g., when stocks reach new highs then supportlevels tend to increase.

In this context, analysis of failure or performance/event data, in anytaxonomy, represents only a subset of the possible ways failure orperformances can be identified. Given any failure or performanceclassification method and operational system, the failure or performanceanalysts need a dynamic system and method to look at reliability datafrom as many perspectives as possible to scan for possible systematicfailure or performance sequences that, if continued or allowed tocontinue, may eventually precipitate a large failure or performanceevent or an event that suggests or otherwise requires a decision to bemade, the latter which will at least be understood in relation toeconomic or financial performance related data.

Analysts need tools that enable them to look at failure or performanceevent relationships and reliability changes by the failure orperformance mode, component, equipment, subsystem, system and otherperspectives in a dynamic fashion. Analysis from these perspectives,based on the given process, equipment, and failure or performanceclassifications represents a best practice in reliability analysis andmeasurement.

Analysts tools for using historical events to identify patterns infailure or performance and significant event data relies on acombination of deterministic methods, and statistical tools, andreliability models. For example, the simple plots of the time betweenfailure or performances (or events) as a function of failure orperformance number can visually show analysts unique insights showingsystematic patterns in failure or performances events identifyingfailure or performance mechanisms not anticipated by the classificationtaxonomy. For example, if this plot shows a sinusoidal-like pattern infailure or performance data, further analysis may indicate that thefailure or performances mainly occurred within one hour of shiftchanges. The fix may be either the adoption of new shift transferprocedures, additional staff training on transfer responsibilities, orboth. The time between failure or performance plot is the insightmechanism that elucidates the operational/organization inefficienciesand the continued analysis using this plot can show if this resolutionmeasures taken were effective.

Another set of tools that are effective in systemic failure orperformance identification are in the field of statistical trendanalysis. These tools use the time between failure or performance dataand the analysis interval to compute the statistically derivedprobability that the time between failure or performances (or events) isgetting smaller (deterioration trend) or larger (improvement trend.)Both types of trends are easily identified given large data sets, butboth types of trend can also be statistically identified with a smallernumber of failure or performances. For example consider a situationwhere there are 5 failure or performances in the early part of theanalysis interval and no failure or performances for the remainder ofthe time. This situation is emblematic of a case where the problem wasidentified early and fixed—no additional failure or performances.Statistical trend analysis could recognize the lack of failure orperformances over the relatively long remainder of the analysis periodand compute a high probability of an improvement trend. Conversely, ifthe same sequence of 5 failure or performances occurred at the end ofthe analysis period a deterioration trend might be shown. The timing ofthe failure or performances, not just between successive events but alsothe position of these failure or performances within the analysisperiod, is valuable information component to identify event trends.

The trend analysis of failure or performances addressed by thisinvention is a valuable tool to assess the validity of the data setswithin the user-defined time interval and within the user-defined groupsto be applied to standard reliability methods such as Weibull Analysis.The primary assumptions that often applied to industrial data are thatthe failure or performance or event data are “independent” and“identically distributed.” These assumptions are represented in thereliability literature as: IID, however similar assumptions can be madefor non-industrial data, e.g., financial and other similar data forwhich trend analysis of failure or performance criteria may be desired.

Data are independent if there is no association between the data values.In practice however, this assumption can be false. For example, considerthis case study: A pump initially failed due to excessive leaking of aseal and was repaired immediately. The next week another seal failed.Seal failure or performances continued to plague the unit. About a monthlater the motor bearings needed to be replaced. When the bearings werereplaced, the alignment of the motor, shaft and coupling were checkedand found to be beyond specifications. The unit was realigned, placedback into service and the frequency of seal failure or performancesdropped nearly to zero. The apparent cause of the seal and bearingfailure or performances was poor alignment. The mis-alignment wore outthe bearings and caused excessive vibrations that caused the series ofseal failure or performances.

Identically distributed data means the probability distribution fromwhich the “time between failure or performances” are derived is notchanging. For failure or performance data where time or some otherrelated variable, such as cycles is used, this means the sameprobability distribution form can be used to model the failure orperformance frequency for the time period under consideration. Thisassumption implies that the chronological order of the data does notcontain any information. In practice the chronological order can containvery important information regarding the future reliability or status ofthe system.

Consider for example the two systems' failure or performance data in thefollowing table:

Time Between Failures Failure Number System #1 System #2 1 10 50 2 20 403 30 30 4 40 20 5 50 10 Mean Time Between Failure 30 30 StandardDeviation 15.8 15.8

System #1 shows that the time between failure or performances isincreasing with failure or performance number or showing a clearimprovement trend. System #2 shows a systematic decrease in the timebetween failure or performances with failure or performance number orexhibits a deterioration trend. This information is obtained fromobserving the chronological order in which the failure or performancesor events occurred. Yet the mean time between failure or performancesand standard deviation of the two very different systems are the same.This example illustrated the importance of examining the chronologicalorder of the failure or performance or events that is an important partof this invention.

There are a several situations that in reality would cause failure orperformances or events to be related or not identically distributed.There can be complex inter-system relationships caused internal andexternal factors that are not always identified, understood, or modeledby the analyst. It is this simple fact that makes the testing of thedata for trends a prudent initial phase in the analysis of reliabilityor event data.

The statistical trend analysis components of this invention aredeveloped to test the data as defined by the analyst for the existenceof trends or patterns. If no trend is identified for a specific groupthen the data is validated as best as possible within the user-definedto be HD. The subsequent optimal interval and maintenance decisionsupport analyses are then technically justified. In the practicalanalysis of failure or performance and event data, these analysissections are nearly always relevant since there are safety,environmental and financial costs and for doing and not doinginspections. In the practical application of this invention, there isoften insufficient data to statistically justify the IID assumptionswhich makes the statistical analysis of trends, the inspection intervaland decision support the analysis of inspection interval optimization istechnically justified. This invention provides analysts with practicaltools to address these issues.

This invention provides analysts with a dynamic system and method forthe trend analysis of value-based data e.g. condition monitoring dataand event based data e.g. failure or performance data. The analyst canenter data in simply formatted data files that can be created inspreadsheet and/or exported to this system from other database programs.

The data values are entered using the taxonomy of the system under studyand no data definition conversions are required. The conditionmonitoring data is compiled and only data values that are within auser-specified time interval are entered into the analysis. The analystscan then combine component of trend data elements to observe trends fora combination of components.

The analyst enters two threshold values where the time of the combineddata groups achievement of these values is important. The systemautomatically computes the forecasted times when the group will achievedthese values in terms of actual dates. The forecasting methods appliedto the user-specified groups are linear, quadratic, and cubic polynomialfits to the group data. Other forecasting techniques could be appliedand the methods used in this and other embodiments are representative ofthe forecasting methodologies that may be applied to the dynamic,user-specified data groupings of value based data.

For event-based data such as reliability or failure or performance data,the same novel dynamic grouping functionality of component IDs intouser-specified groups is applied. Statistical trend analysis techniquesare applied to the data groups to compute in the most preferredembodiment up to four estimates of the probability of the existence of atrend. For failure or performance or event-based data, a trend is notedas either improvement where the time between failure or performances(events) is statistically increasing or deterioration where the timebetween failure or performances (events) is statistically decreasing.The user can visually see the group plots of the time between failure orperformances (events) superimposed on three other trend identificationmethods to assist in the decision process.

Four statistical probably tests are also provided to aid the analyst inidentified the existence or non-existence of a trend, These testsrepresent examples of generally accepted methods for trendidentification but other trend identification methods and embodimentsmay be used alone, supplement or replacement those disclosed hereinwithout departing from the breadth and scope of the invention disclosedherein.

For data groups that have been determined where no trend exists, theinvention enables the analyst to compute, e.g., optimal inspection,analysis, or decision intervals and compare the risk associated betweenstrategies, e.g., two maintenance intervals. The inspection andmaintenance models used in the preferred embodiment are intended to berepresentative and other models are within the scope of the inventiondisclosed herein.

The inspection model produces results for four standard models used inreliability and event analysis: Exponential, Normal, Weibull, andLognormal probability distributions. Optimal inspection (analysis)results are computed using each of these models to provide the analystwith a range of outcomes. This approach is used since the dynamicapplication of data groupings by the analyst plus the lack of sufficientdata may preclude the determination of the technically best model thatfits the data. In practice, reliability results expressed in terms of arange are acceptable in many situations.

Graphical plots of optimal inspection curves as a function of inspectioninterval also provide the analysts with a visual understanding of thesensitivity of the results to test interval changes. The plots can oftensupply the interval information at the level of detail practicallyrequired in most situations.

In a preferred embodiment, the optimal maintenance support decisioninclude relative cost factors for testing, repair, loss of productivitydue to failure or performance, and fixed cost. These four number sum tounity. While these factors are used in the preferred embodiment withrespect to a manufacturing environment, one skilled in the art willreadily understand that different models may be incorporated into thesystem and method, and other factors may be accordingly employed withoutdeparting from the scope and breadth of the invention disclosed andclaimed herein.

The general functional structure of this invention is shown in FIG. 1. Adata files is accessed and based on its format [100], the software isdirected either to the condition-based or failure or performance/eventdata modules. Discussing the condition-based operations first the userspecifies a time interval over which all analysis will be undertaken in[200]. The next module [300] presents the analyst with a listing of alldetailed component IDs that have condition-based data with theprescribed time interval. At this point the user then selects thedesired grouping of the basic component data into larger groups thatwill be analyzed going forward as a single, combined dataset. In [400]the user performs data visualization, trend and predictive analyses. In[500] the analyst can combine component ID if desired to be displayed onthe same plot as separate variables and output this information ifdesired. At any time during the analyses done in [400] and [500] theanalyst may return to [300] to re-group the components or to [200] toanalyze data over a different time interval.

The failure or performance data is filtered based on the time intervalentered in [600]. All data values within the prescribed interval areentered into memory and the user is present with a summary listing ofall component IDs that are available to analysis. The user then combinescomponent ID data that is to be aggregated into larger analysis groupsin [700]. This is a simple, but powerful function to combine failure orperformance/event data to study the reliability or event frequency offailure or performance modes, subsystems or systems comprised of manycomponents. At this point the user can select the trend analysis [800],optimal preventive maintenance interval [900], and the maintenancedecision support modules [1000]. The trend analysis modules enables theanalyst to print the graphical and quantitative results directly.However, the graphics module [1100] is used to show details, e.g., theunavailability, cost (price), and risk curves as a function ofinspection interval. At any time the analyst may either return to entera new time interval [600] or apply new component ID groupings in [700].The dynamic nature of this invention refers to this seamless ability:the re-selection of new component ID groupings.

DETAILED DESCRIPTION

The following disclosure provides many different embodiments, orexamples, for implementing different features of a system and method foraccessing and managing structured content. Specific examples ofcomponents, processes, and implementations are described to help clarifythe invention. These are merely examples and are not intended to limitthe invention from that described in the claims. Well-known elements arepresented without detailed description so as not to obscure thepreferred embodiments of the present invention with unnecessary detail.For the most part, details unnecessary to obtain a completeunderstanding of the preferred embodiments of the present invention havebeen omitted inasmuch as such details are within the skills of personsof ordinary skill in the relevant art.

A preferred embodiment includes a computer implemented method fordynamically analyzing event data for trend analysis of value based datacomprising a computing unit for processing a data set, the computingunit comprising a processor and a storage subsystem an input unit forinputting the data set to be processed, the input unit comprising adatabase of component ID data and event compilations for trend analysiscomprising; an output unit for outputting a processed data set; acomputer program stored by the storage subsystem comprising instructionsthat, when executed, cause the processor to execute following steps of(1) uploading condition, failure or performance or event data using thegiven failure or performance taxonomy, (2) enabling the user to combinefailure or performance or condition groups from different basic elementsto form new failure or performance or condition analysis datasets, (3)perform condition monitoring or valued based trend analysis, (4) performstatistical trend analysis for failure or performance or event-baseddata, compute optimal inspection intervals, and (5) apply a risk-baseddecision-making model for maintenance strategy optimization, and performvarious graphical output functions to communicate and document analysisresults.

The system and method disclosed herein enables the analyst todynamically combine data elements and trend their values together. Trendinformation is shown by plotting the combined data element valuestogether, fitting the data to forecasting functions, and given userspecified target values of the condition values, forecasted times whenthe combined data is anticipated to achieve the user-specifiedthresholds.

The data required for the condition monitoring part of this invention isstructured by records where each record has three parts: a time stamp, amonitored variable name, and the observed value or reading from themonitoring activity. An example of how the condition data can bestructured is shown in FIG. 1.

The usual functions associated with file import are involved next in theinvention. The user enters the data file names and the invention loadsthe data as given in FIG. 2 into memory. These functions are common datainput operations well known computer-related systems.

In one embodiment, in order to compute forecasted times when the dataelements are anticipated to achieve specified values, the user willspecify, for each monitored variable, values that would representsignificant levels for some action to be taken at different prioritylevels. For example, vibration levels that represent abnormal butacceptable levels may be a useful point to begin to schedule overhaul orother maintenance activities. In another example, a stock value ormarket level may similarly be set to address whether a particular actionmay be needed in connection with a security or other asset. This iscalled the Alert level.

This information may easily be compiled into a spreadsheet or file thatis available for use according to the system and method. An example ofthis information is given in FIG. 3. This file is also automaticallyentered along with the condition monitoring data.

A second value that can be interpreted as a higher priority threshold isreferred to as the Action level, e.g., the sale or acquisition of asecurity or asset. In the case of securities, event data relating toIPOs, stock buybacks, hostile acquisitions, to generic stock purchasesand sales, monetary transactions, internet articles, email alerts,articles in online newspapers such as the Wall Street Journal, financialnewsletters, radio & TV transcripts and annual reports, global events,correlations in price movement patterns in response to comments, adviceor observations made by knowledgeable experts regarding a particularsecurity or group of securities, such as the acquisition of anothercompany (which may hold some key IP, know-how or personnel within thatindustry sector) or which is relevant to the future technology directionof the company or losses of some key people or sale of a division whichhad previously been instrumental in promoting a new technologyinitiative for the company, which some believe may be strategic to thecompany, e.g., capturing the “window” of time between when anannouncement is made and when analysts themselves physically state theirinterpretations of announcements containing these similar insights.There may even be a few cases in which a knowledgeable expert may, basedupon certain available facts, predict well ahead of the market thelikely possibility of a forthcoming event, which has significant impactupon stock value. In this case, if this possible eventuality hasdistinctly negative implications on price, it may be worth shorting thestock in advance and in anticipation of the possible eventuality or ifits implications are positive, a purchase of options may be worthwhile(in lieu of gambling on the actual outcome) and/or, in this case, theexpert may introduce a trading rule which anticipates this eventuality(or other possible alternative scenarios). On the other hand,knowledgeable domain experts may state hypothetically such as if X,Y,Zoccurs then Stock A will be a good buy for the following reasons. Thisinformation, in turn, could be used to write a custom rule to anticipatea potential opportunity such that an immediate trade could be triggeredupon such announcement. This rationale as provided (as well asconsidering the identity of the provided of the argument) may itselfcontain useful predictive indicators as to the predicted degree ofsoundness of the rationale. The present invention uses such correlationsto develop prediction models.

The condition-based data main module offers the user three analystoptions as shown in FIG. 4. This screen shows the three main functionsof the condition-based analysis of data for this invention. Once thedata is entered into memory, the analyst sees this screen which beginsthe novel elements of this invention. The analyst is required to enterthe “Time Interval Specification” section.

The invention requires the user to enter Start and Stop dates or times.This interval defines the analysis period and only data elements thathave data values within this period will be included in the analysis.FIG. 5 shows one embodiment of how the user would enter thisinformation. Given the time criteria, the pertinent condition monitoringdatabase is queried to compile of all data records for given data valuesthat have occurred within the prescribed time interval. The stop datecan be a future time to perform trending forecasts and to make thegraphic plots of your data more readable.

Once the data is compiled and successfully validated by comparing themonitored variable names in the data file with the monitored variablenames given in the Alert and Action file, the invention returns theanalyst to the menu screen shown in FIG. 4 and the next stage ofanalysis is performed by selecting the “Trend Analysis” module. Theavailable monitored variables are subsequently listed to communicate tothe analyst what monitoring variables have data values between the startand stop dates. An example of this preliminary analysis structure isgiven in FIG. 6A. Function keys located at the bottom of the screenprovide automated selections for the two straight forward selections ofall data elements in the same group and all in separate groups.

With the available data compiled, the analyst selects which monitoringvariables to be trended. This is done in a systematic fashion by firstplacing a ‘1’ beside the monitoring variables to form group 1, then a‘2’ beside the selected variables to form group 2 and so on. By placingthe same group number beside multiple monitoring variables, the analystselects to trend the combined dataset rather than the individualvariables. The analysts can select to analyze individual variables byplacing a different group number beside each variable.

In the analysis of condition monitoring data, the flexibility to definelarger trend groups by enabling the user to combine data in a stepwise,dynamic fashion is a unique feature of this invention. FIG. 6B shows anexample where the analyst has selected to trend all feed water pumps,steam turbine values, and switchgear values together as three groups.The unselected variables are not involved the subsequent data analysissteps. FIG. 6C shows another example where the analyst has selected totrend each monitoring variable separately.

With the trend groups selected the analyst the invention proceeds toplot the combined trend data sets as a function of time. This is thefirst level in trend identification. The analysts simply observes theplot of the censored and possibly grouped data to visually check forsystematic improvement trends, deterioration trends or recognizablepatterns in the data indicating some transient causal influences.

FIG. 7 shows an example of this visual analysis phase. The vibrationreadings from the Primary and Secondary Feedwater Pumps #1 & #2 readingsare combined into new dataset and plotted together.

With the visual trend information now apparent, the analyst can applyforecasting models to observe the how well the forecasting curves fitthe current data. One embodiment of the invention fits the datasets foreach trend group to linear, quadratic and cubic polynomials. Thesecurves are applied by using function keys as shown in FIG. 7. Othercurve fitting and forecast models can be used and the facility forapplying the predictive models based on the dynamic trend data groupingconstitutes the innovation of this invention.

Based on the Alert and Action values, the forecasting models alsoprovide the times or dates when the trend group models will achievethese values. This functionality is activated by the application ofvarious function keys. FIG. 9 shows an example where of the nature ofthe forecasting results for the feedwater pumps dataset. The analyst nowhas visually studied the data trends and can also observe the quality ofeach forecast model. Based on this information, the analyst gainsqualitative knowledge of the forecast results accuracy. Error limitscould be applied but in practice, the insights of the analyst combinedwith the general quality of the forecast models as assessed also by theanalysts provides a sound basis as to the overall reliability of theforecast results. However, this invention recognizes that the inclusionof prediction error limits in other embodiments of this invention.

To provide the analyst with dynamic forecast information, by applicationof the function keys, the analysts can enter a threshold value and theforecast models will provide the corresponding times when each modelwill achieve the prescribed value. This facility gives the user adynamic and flexible utility to look at trend forecasting for functionvalues that are not a priori entered into the data files. An example ofthis utility is shown in FIG. 9. The user has specified the reading of“9.123” as shown by the arrow in this figure. The dates below the arrowshow the predicted dates by forecasting model (in this case: polynomialcurve fits.)

Based on the application of specific computer keys like the Escape key[ESC], the analyst can return to the initial time screen shown in FIG. 4and select “Graphics” to determine data plotting groups similar tofunctionality performed in the “Trend Analysis” section related tocombining data elements to be trended together. In this case thevariable combinations related to how the analyst wants to present thedata for visual representation only.

Upon entering the “Graphics” module the analyst is presented with thedata variable combination screen present in FIG. 11 similar to FIG. 6.In this however, the grouping of data is only for plotting on the sameof different plots. The data is not combined for analysis. For example,by placing “1” by both feedwater pump variables, the analyst hasselected to plot the condition monitoring values of both pumps on thesame plot as shown in FIG. 12.

Based on the application of specific computer keys like the Escape key[ESC], the analyst can return to the initial time screen shown in FIG. 4and re-compile data over another time interval to perform anotheranalysis. If the user is interested in only re-combining the basic datavariables into new trend groups, then they apply the ESC key until onlyreturning to the trend data screen shown in FIG. 5. This facility toreturn to FIG. 5 to re-select different trend groups provides theanalyst with a flexible, dynamic system to gain more performanceinsights from the available data.

Other function keys are also important for the operation of thisinvention. There are special keys identified for terminating thesession, moving between fields on the screen, various special functionslike showing forecasting function curves, and for screen printing. Thesefunctions are necessary components of this invention since they providethe analyst a way to interface with the operation of the invention.These operations can be performed either by function keys, mouse clickson certain fields, through a combination of both or by using other humaninterface techniques.

Another important functionality of this invention is dedicated to theanalysis of reliability or significant event data. This type of data isgiven by an event name, failure or performance mode label, or otherdescriptor and a date or time that it occurred. Significant events maybe the date of hurricane landfall in the US, when the stock marketachieved a given size, gain or loss, or other events that are ofinterest to track.

The reliability or event data analysis modules require the importationof data in a simple form. The operations associated with loading datainto the system are common, standard operations and familiar to oneskilled in the art. However, the data structure simplicity represents akey element of making the data input process easy to perform for usersof this system.

Reliability data has the same date and descriptor characteristics assignificant event data and may be defined at a failure or performancemode, equipment or system level depending on the severity of theequipment, costs, and the reliability culture of the facility. Also, thedefinition of a failure or performance can vary widely from simpleabnormal condition monitoring readings requiring unscheduled maintenanceto catastrophic loss of system function. This invention utilizes data inwhatever taxonomy used thereby eliminating possibility of data miscodingdue to transforming an individual plants' data definitions into somesoftware required categories.

The data structure required for this part of the invention is a listingof significant events or failure or performances. An example of oneembodiment is shown in FIG. 13. The failure or performance modes in thiscase, are represented by a date (or time stamp) followed by adescription of the corresponding event or failure or performance modename.

With the data file accessed by the system the analyst is presented withthe module selection choices shown in FIG. 14. The “Failure orperformance Group Description” module needs to be selected first tocustomize the data acquisition. The analyst must enter the time intervalfor data selection such as the dates shown in FIG. 15. In practice assituations and plants evolve using all of the historical data presentsnon-intuitive results. More data does not always provide more precisionin forecasting future results. For example, suppose a facilityimplemented a major new reliability program two years ago and managementwants to understand and quantify the program changes if any. Since theprogram has been in effect for two years, the analyst may select a timeinterval that goes back four years. This interval provides one strategyon how to measure if the reliability has indeed changed with the newprogram. Going back say 10 years could give too much weight to the “old”data and present a bias in the results. For this reason, the analyst canselect the time interval over which trend results are judged asrelevant.

Given the time filter, only data records inside this interval arerecorded. An example of the compiled data is shown to the analyst in aformat displayed in FIG. 15A. This data arrangement and thefunctionality it provides represent unique parts of this invention. Atthis point the analyst can study reliability changes at the granularitylevel of their choice by placing a group number component ID beside eachgroup. To facilitate this task for the user, the software has functionkeys that enable the user to group component ID in two ways. Onefunction key places all component IDs in a single group and anotherassigns each component ID in different group number. These two choicesare presented in FIGS. 15B and 15C respectively.

The automated choice of placing component IDs into their distinct groupsmay be used for example if the analyst is scanning for changes inreliability over the entire system configured in the dataset at the mostdetailed level defined by the data. Combining all of the componentstogether into one group may be used if the analyst is interested inreliability changes at the macroscopic system, unit, plant, company orover the entire span of assets where event data has been recorded.

These two choices are only two of several options available to theanalyst. The analysts can choose what component IDs to combine intodistinct groups that represent reliability items of current interest.This component combination functionality enables the data to be scannedmany ways for trends and patterns in event data that may not be obviousfrom the basic component data. For example, there either may beinsufficient data or no trend results for individual component IDs butcombining a subset of them into a group may show a trend or otherpattern in the data.

For example referring to FIG. 17, suppose that the analysis of componentdata in either has insufficient data to be studied alone or there is noascertainable trend or pattern information observed. However, thecombined grouping shown here is just but one choice the analyst can maketo observe reliability changes related to looking at the data from onepoint of view or strategy:

-   -   Group #1 All failure or performances for the ECC values in both        the failure or performance-to-open (FTO) and        -   the failure or performance-to-close (FTC) modes.    -   Group #2: All seal leak events for the pumps.    -   Group #3: All compressor bearing failure or performance events        for the turbines.    -   Group #4: All motor failure or performances.    -   Group #5: All turbine failure or performances.

Another embodiment of this invention would be to automate the groupformation process to systematically or randomly examine reliabilitychanges between all or some pre-defined set of component IDs. Thecombination method could be related to the data taxonomy, the number ofdata items in each component ID or some external criteria determined bythe analyst.

Once the component IDs have been combined into user-selected groups, thefirst analysis phase, is to combine the failure or performance timesinto one dataset for each group. This is depicted graphically for eachgroup in FIGS. 16A-E. Each grouping represents a different example ofthe application of this invention. In these figures. The failure orperformance times for each component ID are indicated by markers onseparate lines. The group time between failure or performance dataset ineach case is produced as the projection of all component failure orperformances into a single timeline. This projection is a non-intuitivefunction that is well-known to analysts skilled in aspect of theseoperations. Each group example here represents a different example ofthe trend analysis functionality and utility.

Group 1 data representing all ECC Valve failure or performances at firstglance, shows possibly an improvement trend. There appears to be morefailure or performances in the first half of the time period than in thesecond half. However, the difference may be due to normal statisticalvariability inherent in valve reliability and the analyst needs tounderstand the operating conditions of the equipment over the timeinterval. If the operating environment was different in the first halfthan the second half then the analysis of failure or performances overthe entire interval needs to be interpreted taking this fact intoaccount.

Group 2 shows that the seal leaks on pumps A, B, & C. occurred early inthe time period then for the large majority of the internal there wereno failure or performances. Assuming the operating conditions of thepumps hasn't changed then is it is apparent that the root cause offailure or performances has been identified and the problem corrected.The correction may be a change in the preventive maintenance orapplication of new predictive maintenance technologies that identifyseal deterioration. With this knowledge, maintenance and operationsstaff can schedule seal changes into planned maintenance schedulesthereby eliminating the safety, costs, and environmental issues that canbe associated with unscheduled seal failure or performances. This teststatistics applied in this invention identify this improvementreliability trend. Improvement trends can exist in two ways. There maybe a large amount of failure or performances or events where the timebetween events is consistently getting or as in the situation describedby Group 2, failure or performances occur early in the time period thenno failure or performances are present for the large portion of the timeinterval.

Group 3, representing all compressor bearing failure or performancesrepresents the opposite situation. In Group 2, all failure orperformances occurred in the beginning of the time interval. In Group 3,all failure or performances occur in the second half of the timeinterval. This situation is emblematic of a deterioration trend.

Groups 4 and 5 represent common situations where there are sufficientfailure or performances for the application of several trend analysisstatistics and the visually appears difficult to visually ascertain anytrend information. It is important to note that the visualrepresentation of the time between failure or performances of theuser-specified aggregation of component IDs is time consuming,subjective, and can produce different conclusions from differentanalysts. The purpose of FIGS. 16A-E is to provide a connection betweenthe trend results and the test statistics. The plots of this informationhowever represent important data for developing trend conclusions aswill be shown in subsequent paragraphs describing this invention.

Once the analyst defines the component ID groupings, the inventionperforms the failure or performance or event compilations and allows theuser to review a trend analysis summary for each group, including:

-   -   statistical estimates for the probability of the trend in each        group,    -   mean time between failure or performances, in days, for each        group,    -   predicted time until next failure or performance, in days, for        each group.        An example of this information for the five selected groups        shown in FIGS. 16A-E, is presented in FIG. 18.

This analysis phase presents the user with a summary of trendinformation as s pre-screening tool. The user selects from thispreliminary trend information which groups are of interest to studyfurther. The user can use a special function key to shortcut the manualselection process to automatically select all groups for further study.

The failure or performance statistics applied in this example arepractical, industry-accepted measures of reliability growth or trendidentification. Since failure or performance or event data representstochastic points in time, there is an inherent uncertainty withinferring trend-based conclusions. Consequently the invention providesthe user with the probability or likelihood estimates of the trendexistence. It is important to note, however, that although thesepredictions employ highly reliable statistical models, they are stillestimates. Even when the probability is as high as 0.99 (99%), there isno guarantee the predicted time until the next failure or performancewill be accurate. The final decision that a trend or pattern does ordoes not exist must be made by the person familiar with the systems,components, and failure or performance IDs under analysis. The inventionpresents the statistical trend information in both graphical andnumerical formats from which such trend decisions can be made. There aremany trend identification models that could be included in variousembodiments and are examples used here are intended to represent oneapplication. For example, the identification of data trends implies aspecific pattern in the data and another identification method thatcould be used in the invention is the Maximal Information Coefficientthat can identify complex patterns in data that are not achievable withstandard statistical methods. The test statistics used in thisdescription of the invention are for illustration only. Other trendidentification tools and statistical tests could either be used assupplements to or replacements for the ones applied in this embodimentof the invention.

An example of the summary trend information shown in FIG. 19 applies twostatistical trend methods: the Laplace and the Military Handbook-189tests statistics. The methods are used to produce two separateprobability estimates of the existence of either an improvement ordeterioration trend.

The Laplace and MIL-HBK-189 tests used in this embodiment of theinvention are standard methods in reliability growth analysis that canidentify trends where the rate of occurrences of failure or performancesor events varies with time compared to a statistical process where it isconstant. These tests use a specific formula for the rate of occurrenceof failure or performances. If R(t) is the rate of occurrences offailure or performances at time, t, the general equation used in thederivation of these two test statistics is:R(t)=λβ exp(β−1)

Beta (β) is called the growth parameter. If β=1, then the rate ofoccurrences of failure or performances is a constant and the systemreliability does not change with time. If β>1, the system reliability isdecreasing and a deterioration trend is present. If β<1, then the rateof occurrence of failure or performances is decreasing and animprovement trend is present. The reciprocal of R(t) can be interpretedas the instantaneous mean time between failure or performances at timet. Both β and λ are computed from the data and the total time periodunder analysis using methods common to those skilled in stochasticprocess methods.

The Laplace Test was designed to test for the existence of possibleimprovement or deterioration trends by comparing its results to aPoisson process where the rate of occurrence of failure or performancesis a constant. It is one of relatively few tests found to be useful forsmall datasets {n (number of failure or performances)>4}, as well aslarge datasets. It utilizes the time between failure or performances andthe total time period under analysis. The times between failure orperformances are added together to produce a sequence of data valueswhich are the total times to failure or performance measured from thetime of the first observed failure or performance. If t₁<t₂<t₃< . . .<t_(N) are the times to failure or performance and T is the total timeperiod (t_(N)≤T), then the Laplace statistic, U, is conformsapproximately to a normal distribution.

$U = \frac{{\sum\limits_{i - 1}^{N}t_{i}} - {\frac{1}{2}{NT}}}{T\sqrt{\frac{N}{12\;}}}$

The MIL-HBK-189 statistic is a chi-squared distributed statistic with 2Ndegrees of freedom using the same data as the Laplace formula. The testis also designed to identify improvement and deterioration trendscompared to a Poisson (constant rate of occurrences of failure orperformances) process.

$\chi_{2N}^{2} = {2{\sum\limits_{i = 1}^{N}{\ln\left( \frac{T}{t_{i\;}} \right)}}}$

Predictions for the time to the next failure or performance or event orPTNF, can be important information and the current embodimentinformation provides two estimates. The first prediction utilizes theequation given in paragraph 67 with the parameters β and λ computed fromfor each group's data using methods common to analysts skilled in thisart. To obtain the time to the N+1^(st) failure or performance or event,the equation is integrated from t_(N) (which is known to t_(N+1)). Theresult produces the first estimate of the predicted time to the nextfailure or performance or event given by

$t_{n + 1} = \left( {t_{n}^{\beta} + \frac{1}{\lambda}} \right)^{1/\beta}$

The Mean Time Between Failure or performances or MTBF is computed as thesimple average of the time between failure or performance data values.The difference between the MTBF and the PTNF can provide additionaltrend insight. The difference between these values represents thestrength or steepness of the trend. The Laplace and Mil-HBK-189probabilities provide the analyst with the likelihood that a trendexists but does not provide any information on the nature of the trend.It is entirely possible that the test statistic probabilities could bothindicate a high confidence of either an improvement or deteriorationtrend but if the PTFN is about the same value of the MTBF, then thetrend is nearly flat. This situation suggests that there is a highconfidence of a very slow or no trend in the failure or performance orevent group. If the PTNF is judged significantly larger(smaller) thanthe MTBF then the improvement trend is interpreted as a steepincrease(decrease) in reliability.

Analyzing the summary information on FIG. 18, the user selects thegroups to study in more detail by using keyboard inputs as directed bythe function keys at the bottom of the screen. The more detailedanalysis begins with a graph of the time between failure or performancesfor the grouped data as a function of failure or performance numbersuper-imposed on several analysis results. Two examples are discussed toexhibit some of the functionality of this invention.

The first case uses Group 2 whose failure or performance data timelineis plotted in FIG. 17B. This situation presents a practical situationwhere the group experienced several failure or performances early in theanalysis period but then had no failure or performances for theremainder of the time period. Notice first that from the summary trendinformation shown in FIG. 18, the MTBF is 27 days but the PTNF is 522days. This difference may represent a very steep improvement trend. Justanalyzing the failure or performance data alone without considerationfor when inside the time period the failure or performances occurred mayprovide insufficient information for an accurate identification oftrends. Like in this group, the time between failure or performances forthe dataset itself shows a deterioration trend but the fact that therewere no failure or performances for the large majority of the timeperiod is not included in the regression since there were no failure orperformances. The plot shown in FIG. 19 for the time between failure orperformances versus failure or performance number shows these factsvisually. The straight line plot is computed as a simple linearregression fit to the data and the line provides an indicator (based ononly the data) as to the increase or decrease in the time betweenfailure or performances. This line is extrapolated to the N+1^(st)failure or performance to provide a visual estimate of the predictedtime to the next failure or performance.

The nonlinear estimate for the PTNF computed from [0060] is depicted onthe data plot using by the symbol

at the N+1^(st) value on the time between failure or performance orhorizontal axis.

The fourth visual trend indicator is computed as the MTBF as a functionof failure or performance number. The closer this function is to flat orzero slope, the more valid the assumption of a constant failure orperformance rate or no trend. The validity of this assumption can bejudged by simply looking at the dotted line varies or does not vary as afunction of failure or performance number. This is a subjective by verypowerful information for the analyst in the identification of trends orno trends. However in this case without new data, this indicator canalso be misleading.

By application of the function keys as shown along the bottom of FIG.19, the analyst can select the next level of analysis detail. An examplefor Group 2 is shown in FIG. 20. The screen content presents thequantitative results of what was visualized in the preceding view. Thefirst two test statistics, Laplace and Mil-HBK-189, viable for smalldata sets, are powerful indicators that a trend may be present. TheSpearman Rank Correlation rank test, labeled Rank Test in thisembodiment, is a common trend test statistic mostly applied to largedatasets. It is a nonparametric test that inherently does rely on thedistribution of the underlying data and its technical development andrepresentation is known to analysts skilled in this art. The linearregression probability is computed from the probability that the sign ofthe value of the slope is either positive or negative. The four trendtests represent only one embodiment of trend test statistics. Othertrend indicators could be used instead of or in addition to the onesshown here.

The Laplace and Mil-HBK-189 tests that consider the failure orperformance and time period data together both indicate a highlikelihood of improvement. However, the Rank Test and Linear Regressionestimates that use only the failure or performance data values, show ahigh likelihood for deterioration. This result suggests that when thefailure or performances occur within the time interval in addition towhen the failure or performances relative to each other can be importantinformation in identifying trends.

The PTNF values depicted up to this point in the analysis have been interms of the time from the last failure or performance. This frame ofreference is difficult to apply in practice. The next section in FIG. 20translates the PTNF values to actual dates using the date values in thegroup dataset so the analyst is informed directly as the specific datethe models predict the next failure or performance. These values areestimates, and the analyst must factor other subjective information intothe confidence place in these dates. Ranges could also be entered yetthe ranges could be too wide to provide any meaningful information. Thisembodiment provides the model direct estimates and relies on theinsights and judgment of the analyst to conclude their accuracy.

The second example is for Group 5 in FIG. 18. The data timeline is shownin FIG. 17E. The time between failure or performances versus failure orperformance number plot and PTNF estimates are shown in FIG. 21. Thedata points, shown in FIG. 21, provide the analyst with valuableinsights about the timing characteristics of the failure or performancesor events in Group 5. In this case, there are a relative large number offailure or performances and apparently no time bias from observing FIG.17E or FIG. 21. This plotting format can depict distinct patterns in thetime between failure or performance data that are possible clues to theroot causes of the failure or performances. This subjective informationrepresents extremely valuable data-driven evidence to direct additionalresearch on root cause analysis or other activities designed tounderstand the fundamental causes of failure or performance or eventfrequency. The patterns may not be recognized by classical statistics.This is why the visual representations are an important part of thisinvention. Analysts in collaboration with system experts can use thefailure or performance pattern information to learn more about thecomplex nature of system failure or performance or event frequency.

Advancing to the next level of detail by application of the functionkeys shows a different results than observed for Group 2. The trendanalysis detail is shown in FIG. 22. In cases where the trendprobabilities are high >90% and all of the statistics agree on thenature of the trend (improvement or deterioration), trend identificationis relatively simple. However, the example depicted in FIG. 22 finds thetrend probabilities are all relatively low, the trend types do notagree, and the visual plot show characteristics of a constant failure orperformance rate. In this case, the analyst may conclude that there isno trend in the data. Another way of stating this fact in practicalterms for the turbine over-speed and lube oil failure or performancemodes, is to conclude the reliability of these systems has not changedover the analysis time interval.

The invention in this the preferred embodiment enables the user tocombine component failure or performance to analyze reliability atvarious levels of system hierarchy. The predicted times to the nextfailure or performance (PTNFs), the four trend probabilities withassociated trend information, and the visual plots of group time betweenfailure or performance data together provides the analyst with a robustframework for trend identification.

The analyst may advance one screen further in results detail to studythe reliability parameters used in the test statistics and linearregression. This data may be useful for the additional more detailedcalculations and they are simply listed in tabular form. Thispresentation demonstrates the utility of this invention in computingreliability parameters that can be applied to other calculations thatmay be developed based on the trend information learned from thepreceding analysis screens. Additional insights can be achieved byvarying the time interval and dynamic re-grouping of the basic failureor performance or event data into analysis groups. This iterativeprocess of studying results of a given strategy and going back andperforming additional trend analyses with different component IDgroupings and/or different time is novel and part of this invention. Atany point in the analysis the user may return to the main module screenby use of mouse clicks, the ESC Key, or the CTRL-E function key.

With the trend properties of the groups analyzed the user may return tothe main screen shown in FIG. 14 to access the other computationaloptions. Typically, the next step in the analysis of event data is tocompute optimal inspection intervals for the user pre-defined failure orperformance or event groups. Based on the trend identification results,the analyst may select only groups with no identified trends for studyor include others where a trend may possibly be present but clearconclusion can be made. In some cases, the trend analysis results maynot be conclusive. The Optimal Inspection interval module first requiresthe analyst to enter average testing and repair timed for each group.These parameters represent the data required for the particularinspection interval model used in this embodiment of the invention.Different embodiments may use different inspection interval modelsrequiring different parameters. An example of the data input format forthis embodiment is shown in FIG. 23.

The special function keys ALT-G and ALT-M are designed to provide theanalyst with group and component ID information. ALT-G lists thecomponent IDs within a specific group and ALT-M provides a descriptionof a component ID.

To include economic factors in the optimal inspection intervalcalculation the user enters relative cost factors as shown in FIG. 24.The factors in this embodiment define the relative amount of totalexpense for each group associated with testing, repair, loss ofproductivity due to failure or performance, and fixed costs if any.These relative cost factors serve a weighting factors {c_(i)} for thecost and risk functions used to compute optimal (minimum cost and risk)inspection intervals.

This embodiment of the invention incorporates an optimal inspectionmodel that is related to the following equations. This is one of severalmodels that could be employed in other embodiments of this invention.Q(τ) is the group unavailability (probability of system not functioningon a random demand) as a function of test interval τ, C(τ), and R(τ) arethe group cost and risk functions respectively:

${Q(\tau)} = {\frac{T}{\tau} + {\left( {1 + \frac{R}{\tau}} \right)*\left\{ \frac{1}{\tau} \right\}{\int_{0}^{\tau}{{F(s)}d\; s}}}}$${C(\tau)} = {{\frac{T}{\tau}c_{testing}} + {\left( {c_{failure} + {\frac{R}{\tau}c_{repair}}} \right)*\left\{ \frac{1}{\tau} \right\}{\int_{0}^{\tau}{{F(s)}d\; s}}} + c_{fixed}}$R(τ) = Q(τ) * C(τ)

-   -   where: τ=test interval        -   F(s)=system failure or performance probability by time s        -   T=average testing time        -   R=average repair time    -   c_(testing), c_(repair), c_(failure or performance),        c_(fixed)=relative cost factors

Once the user enters the testing times(T), the repair times(R), and therelative cost factors, the invention computes the optimal inspectionintervals that minimizes unavailability, cost, and risk and presentsthis information in a summary table as shown in FIG. 25. For each group,the system computes the range of optimal interval results from the fourprobability distributions: exponential, weibull, gamma, and lognormalapplied to modeling system reliability, F(s).

The analyst can then select the group to observe the computationaldetails for all failure or performance probability models. An example ofthis information for Group 5 is presented in FIG. 26.

Once the optimal interval details are known the analyst may want toexamine of the sensitivity of the results. The simplest way toaccomplish this task is to display the plots of the Q, C, & R functionsas a function of test interval. These operations are performed in thegraphics module from the module selection screen shown in FIG. 14.

Upon entering the “Graphics” module, the analyst is presented with atable that enables the selection of specific groups to view the plots.When the plot selection is performed, the maximum test interval for theplots is then entered to complete the plot data input process as shownin FIG. 27.

The plotting results for the selected group is presented in FIG. 28. Thetypical unavailability, cost, and risk plots show this type ofstructure. The portion of the curve with negative slope is emblematic ofover testing where the excessive testing is causing the system to beunavailable. The portion of the curve with positive slope signifiesunder-testing. The model assumption here is that testing at a higherinterval frequency (or short testing intervals) would catch theprecursors to failure or performance. The visual representation of theanalytical results for the dynamic component ID groupings providesvaluable additional insights on how to plan inspection intervals forsystems, subsystems, equipment, and components.

The “Maintenance Decision Support” module uses the risk functions R(t)developed in the “Optimal Inspection Interval” section. This modulecontrasts the risk associated between maintenance strategies for auser-specified group. Given the group selection and the entry of theaverage testing and repairs times, the analyst is presented with theopportunity to enter different relative cost factors that would berepresented of a different maintenance strategy. An example is shown inFIG. 29.

This invention provides a practical framework for studying the effect ofdifferent maintenance scenarios. The relative cost factors are loadedwith the factors used previous in the analyses although they can bechanged here to reflect a different comparison base. A possible strategyfor the selection of the parameters shown in FIG. 29 is that ScenarioTwo reflects a change in inspection activities. The relative cost oftesting is increased to reflect the application of new predictivetechnology tools and the added staff training. It is estimated that theadded testing costs will cause a significant reduction in unplannedmaintenance and a slight repair in repair costs. The question is doesthe proposed maintenance strategy represent a higher or lower riskrelative to the base scenario.

The detailed scenario results are shown in FIG. 30. The user then entersthe inspection interval which represents the value of the inspectioninterval used to evaluate the two risk functions: t₀. The inventioncomputes the ratio:

$\mathcal{S} = {\frac{R\left( {t_{0},\left\{ c \right\}_{1}} \right)}{R\left( {t_{0},\left\{ c \right\}_{2}} \right)}.}$If

<1, Scenario One has the lower risk and if

>1, then Scenario Two has the lower risk.

The results from the decision support analysis presented in FIG. 31provide the analyst with as much information as possible in the complexof risk function optimum test intervals. The value of

for each failure or performance model are computed, and the scenariorelative cost factors for each scenario are presented for reference.

The system and method disclosed herein provides a powerful, yeteasy-to-use tool for analysis of your installation's condition readingand failure or performance data. With the condition monitoring featuresallows a user to: automatically utilize powerful statistical tools togather evidence that a trend does or does not exist among the datavalues, use the trend analysis results to make predictions of when theequipment readings will achieve critical values you've establishedinteractively select a reading value and observe the time when thereading is forecasted to reach that value, transfer data simply fromspreadsheet or database software, select the time intervals to performthe trend analysis, combine data from different equipment to enabletrending a “family” as well as each individual piece of equipment orcomponent.

With the failure or performance data analysis features of the system andmethod a user can—compare multiple different statistical analysismethods of trend analysis observe failure or performance trends bycomponent or by sub-system, grouped as user specified or for the entiresystem, for the time period selected, determine optimal test intervals,based on user specified criteria, e.g., cost, risk, or probability,produce graphic representations of failure or performance patterns,simulate effects of changes based on user specified criteria, e.g.,testing, repair, failure or performance, and fixed cost allocation andcreate and update databases to record failure or performances.

We claim:
 1. A computerized method, the method comprising: programming,by a computer processor, a computer graphical user interface of acomputer device of a user to allow the user to select, in real-time, aplurality of computer entities, displayed on the computer graphical userinterface, into at least one ad hoc dynamic grouping; wherein theplurality of computer visual entities of the computer graphical userinterface are representative of a plurality of physical components;wherein each physical component of the plurality of physical componentsis associated with at least one value-based data item representative ofat least one of: at least one condition of a respective physicalcomponent and at least one event related to the respective physicalcomponent; determining, by the computer processor, a data taxonomy forthe at least one ad hoc dynamic grouping; wherein the data taxonomy isdetermined without performing data definition conversion for eachvalue-based data item associated with each physical component of theplurality of physical components; wherein the data taxonomy isconfigured to classify each value-based data item associated with eachphysical component of the plurality of physical components; receiving,by the computer processor, via the computer graphical user interface, auser selection, identifying at least: 1) the at least one ad hoc dynamicgrouping and 2) a specified time interval; obtaining, by the computerprocessor, component data for the at least one ad hoc dynamic groupingbased on the specified time interval, wherein the component datacomprises a plurality of value-based data items associated with theplurality of physical components of the at least one ad hoc dynamicgrouping; generating, by the computer processor, based on the datataxonomy, a single dataset file from the component data associated withthe plurality of physical components of the at least one ad hoc dynamicgrouping; applying, by the computer processor, at least one statisticaltrend analysis technique to the single dataset file associated with theplurality of physical components of the at least one ad hoc dynamicgrouping to determine at least one trend in the component data;transforming, by the computer processor, based at least in part on theat least one trend in the component data, the component data into anoperational group model for the at least one ad hoc dynamic grouping ofthe single dataset file; wherein the operational group model for the atleast one ad hoc dynamic grouping of the single dataset file comprises aplurality of alert levels and a plurality of action levels; and causing,by the computer processor, based at least in part on the operationalgroup model, to generate at least one of: i) an optimal preventivemaintenance schedule for at least one physical component of theplurality of physical components of the at least one ad hoc dynamicgrouping; ii) at least one first alert comprising first data predictinga first time period to a next failure of the at least one physicalcomponent of the plurality of physical components of the at least one adhoc dynamic grouping and, optionally, at least one first decision thatsuggests or requires at least one first change related to the nextfailure; iii) at least one second alert comprising second datapredicting a second time period to a next event associated with the atleast one physical component of the plurality of physical components ofthe at least one ad hoc dynamic grouping and, optionally, at least onesecond decision that suggests or requires at least one second changerelated to the next event; iv) at least one third alert comprising thirddata predicting a third time period between failures of the at least onephysical component of the plurality of physical components of the atleast one ad hoc dynamic grouping and, optionally, at least one thirddecision that suggests or requires at least one third change related tothe failures; v) at least one fourth alert comprising fourth datapredicting a fourth time period between events associated with the atleast one physical component of the plurality of physical components ofthe at least one ad hoc dynamic grouping, and, optionally, at least onefourth decision that suggests or requires at least one fourth changerelated to the events; and vi) any combination thereof.
 2. The method ofclaim 1, wherein the plurality of physical components and the at leastone event are selected from the group consisting of: 1) physicalcomponents and events associated with at least one manufacturing plant,2) physical components and events associated with at least one of thefollowing industries: medical, airline, social media,telecommunications, oil and gas, chemicals, hydrocarbon processing,pharmaceutical, and biotechnology, and 3) physical components and eventsassociated with at least one securities market, 4) events associatedwith weather, and 5) physical components and events associated with atleast one of housing and commercial real estate markets.
 3. The methodof claim 1, wherein the at least one value-based data item isrepresentative of component ID data.
 4. The method of claim 1, whereinthe optimal preventive maintenance schedule comprises a preventivemaintenance interval.
 5. The method of claim 1, wherein at least one ofthe at least one first change, the at least one second change, the atleast one third change, and the at least one fourth change comprises atrigger to buy or sell.
 6. A computer system, comprising: a computerprogram stored on a non-transitory storage subsystem of the computersystem, comprising instructions that, when executed, cause a processorto execute the steps of: receiving, a computer graphical user interfaceof a computer device of a user, a first user selection of a plurality ofcomputer entities, displayed on the computer graphical user interface,into at least one ad hoc dynamic grouping; wherein the plurality ofcomputer visual entities of the computer graphical user interface arerepresentative of a plurality of physical components; wherein eachphysical component of the plurality of physical components is associatedwith at least one value-based data item representative of at least oneof: at least one condition of a respective physical component and atleast one event related to the respective physical component;determining a data taxonomy for the at least one ad hoc dynamicgrouping; wherein the data taxonomy is determined without performingdata definition conversion for each value-based data item associatedwith each physical component of the plurality of physical components;wherein the data taxonomy is configured to classify each value-baseddata item associated with each physical component of the plurality ofphysical components; receiving, via the computer graphical userinterface, a second user selection, identifying at least: 1) the atleast one ad hoc dynamic grouping and 2) a specified time interval;obtaining component data for the at least one ad hoc dynamic groupingbased on the specified time interval, wherein the component datacomprises a plurality of value-based data items associated with theplurality of physical components of the at least one ad hoc dynamicgrouping; generating, based on the data taxonomy, a single dataset filefrom the component data associated with the plurality of physicalcomponents of the at least one ad hoc dynamic grouping; applying atleast one statistical trend analysis technique to the single datasetfile associated with the plurality of physical components of the atleast one ad hoc dynamic grouping to determine at least one trend in thecomponent data; transforming, based at least in part on the at least onetrend in the component data, the component data into an operationalgroup model for the at least one ad hoc dynamic grouping of the singledataset file; wherein the operational group model for the at least onead hoc dynamic grouping of the single dataset file comprises a pluralityof alert levels and a plurality of action levels; and causing, based atleast in part on the operational group model, to generate at least oneof: i) an optimal preventive maintenance schedule for at least onephysical component of the plurality of physical components of the atleast one ad hoc dynamic grouping; ii) at least one first alertcomprising first data predicting a first time period to a next failureof the at least one physical component of the plurality of physicalcomponents of the at least one ad hoc dynamic grouping and, optionally,at least one first decision that suggests or requires at least one firstchange related to the next failure; iii) at least one second alertcomprising second data predicting a second time period to a next eventassociated with the at least one physical component of the plurality ofphysical components of the at least one ad hoc dynamic grouping and,optionally, at least one second decision that suggests or requires atleast one second change related to the next event; iv) at least onethird alert comprising third data predicting a third time period betweenfailures of the at least one physical component of the plurality ofphysical components of the at least one ad hoc dynamic grouping and,optionally, at least one third decision that suggests or requires atleast one third change related to the failures; v) at least one fourthalert comprising fourth data predicting a fourth time period betweenevents associated with the at least one physical component of theplurality of physical components of the at least one ad hoc dynamicgrouping, and, optionally, at least one fourth decision that suggests orrequires at least one fourth change related to the events; and vi) anycombination thereof.
 7. The system of claim 6, wherein the plurality ofphysical components and the at least one event are selected from thegroup consisting of: 1) physical components and events associated withat least one manufacturing plant, 2) physical components and eventsassociated with at least one of the following industries: medical,airline, social media, telecommunications, oil and gas, chemicals,hydrocarbon processing, pharmaceutical, and biotechnology, and 3)physical components and events associated with at least one securitiesmarket, 4) events associated with weather, and 5) physical componentsand events associated with at least one of housing and commercial realestate markets.
 8. The system of claim 6, wherein the at least onevalue-based data item is representative of component ID data.
 9. Thesystem of claim 6, wherein the optimal preventive maintenance schedulecomprises a preventive maintenance interval.
 10. The system of claim 6,wherein at least one of the at least one first change, the at least onesecond change, the at least one third change, and the at least onefourth change comprises a trigger to buy or sell.